Feature-based registration method

ABSTRACT

Methods for registering a three-dimensional model of a body volume to a real-time indication of a sensor position that involve analyzing scanned and sensed voxels and using parameters or thresholds to identify said voxels as being either tissue or intraluminal fluid. Those voxels identified as fluid are then used to construct a real-time sensed three-dimensional model of the lumen which is then compared to a similarly constructed, but previously scanned model to establish and update registration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. application Ser. No. 12/476,976filed Jun. 2, 2009, which claims priority to U.S. ProvisionalApplication Ser. No. 61/058,470 filed Jun. 3, 2008, which are herebyincorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

Breakthrough technology has emerged which allows the navigation of acatheter tip through a tortuous channel, such as those found in thepulmonary system, to a predetermined target. This technology comparesthe real-time movement of a sensor against a three-dimensional digitalmap of the targeted area of the body (for purposes of explanation, thepulmonary airways of the lungs will be used hereinafter, though oneskilled in the art will realize the present invention could be used inany body cavity or system: circulatory, digestive, pulmonary, to name afew).

Such technology is described in U.S. Pat. Nos. 6,188,355; 6,226,543;6,558,333; 6,574,498; 6,593,884; 6,615,155; 6,702,780; 6,711,429;6,833,814; 6,974,788; and 6,996,430, all to Gilboa or Gilboa et al.; andU.S. Published Applications Pub. Nos. 2002/0193686; 2003/0074011;2003/0216639; 2004/0249267 to either Gilboa or Gilboa et al. All ofthese references are incorporated herein in their entireties.

Using this technology begins with recording a plurality of images of theapplicable portion of the patient, for example, the lungs. These imagesare often recorded using CT technology. CT images are two-dimensionalslices of a portion of the patient. After taking several, parallelimages, the images may be “assembled” by a computer to form athree-dimensional model, or “CT volume” of the lungs.

The CT volume is used during the procedure as a map to the target. Thephysician navigates a steerable probe that has a trackable sensor at itsdistal tip. The sensor provides the system with a real-time image of itslocation. However, because the image of the sensor location appears as avector on the screen, the image has no context without superimposing theCT volume over the image provided by the sensor. The act ofsuperimposing the CT volume and the sensor image is known as“registration.”

There are various registration methods, some of which are described inthe aforementioned references. For example, point registration involvesselecting a plurality of points, typically identifiable anatomicallandmarks, inside the lung from the CT volume and then using the sensor(with the help of an endoscope) and “clicking” on each of thecorresponding landmarks in the lung. Clicking on the landmarks refers toactivating a record feature on the sensor that signifies theregistration point should be recorded. The recorded points are thenaligned with the points in the CT volume, such that registration isachieved. This method works well for initial registration in the centralarea but as the sensor is navigated to the distal portions of the lungs,the registration becomes less accurate as the distal airways are smallerand move more with the breathing cycle.

Another example of a registration method is to record a segment of anairway and shape-match that segment to a corresponding segment in the CTvolume. This method of registration suffers similar setbacks to thepoint registration method, though it can be used in more distal airwaysbecause an endoscope is not required. The registration should beconducted more than once to keep the registration updated. It may beinconvenient or otherwise undesirable to require additional registrationsteps from a physician. Additionally, this method requires that a goodimage exists in the CT volume for any given airway occupied by thesensor. If for example, the CT scan resulted in an airway shadowed by ablood vessel, for example, the registration will suffer because theshape data on that airway is compromised.

An alternative registration method known as “Adaptive Navigation” wasdeveloped and described in U.S. Published Application 2008/0118135 toAverbuch et al., incorporated by reference herein in its entirety. Thisregistration technique operates on the assumption that the sensorremains in the airways at all times. The position of the sensor isrecorded as the sensor is advanced, thus providing a shaped historicalpath of where the sensor has been. This registration method requires thedevelopment of a computer-generated and automatically or manuallysegmented “Bronchial Tree” (BT). The shape of the historical path ismatched to a corresponding shape in the BT.

Segmenting the BT involves converting the CT volume into a series ofdigitally-identified branches to develop, or “grow,” a virtual model ofthe lungs. Automatic segmentation works well on the well-defined, largerairways and smaller airways that were imaged well in the CT scans.However, as the airways get smaller, the CT scan gets “noisier” andmakes continued automatic segmentation inaccurate. Noise results frompoor image quality, small airways, or airways that are shadowed by otherfeatures such as blood vessels. Noise can cause the automaticsegmentation process to generate false branches and/or loops—airwaysthat rejoin, an occurrence not found in the actual lungs.

It would be advantageous to provide a registration method that isautomatic and continuous, and has an increased accuracy potential thatis achieved without requiring any steps to be taken by a physician.

SUMMARY OF THE INVENTION

In view of the foregoing, one aspect of the present invention provides afeature-based registration method. When the CT scans are taken, the CTmachine records each image as a plurality of pixels. When the variousscans are assembled together to form a CT volume, voxels (volumetricpixels) appear and can be defined as volume elements, representingvalues on a regular grid in three dimensional space. Each of the voxelsis assigned a number based on the tissue density Housefield number. Thisdensity value can be associated with gray level or color using wellknown window-leveling techniques.

One aspect of the present invention relates to the voxelization of thesensing volume of an electromagnetic field by digitizing it into voxelsof a specific size compatible with the CT volume. Each voxel visited bythe sensor can be assigned a value that correlates to the frequency withwhich that voxel is visited by the sensor. The densities of the voxelsin the CT volume are adjusted according to these values, therebycreating clouds of voxels in the CT volume having varying densities.These voxels clouds or clusters thus match the interior anatomicalfeatures of the lungs.

Another aspect of the present invention is to provide a plurality ofparameters that a particular voxel of the CT volume must meet beforebeing considered as a candidate for matching to a corresponding voxel inthe sensor sensing volume. For example, the voxel could be required tomeet parameters such as: 1) falls within a particular density range, 2)falls within a predefined proximity from a currently accepted(registered) voxel, 3) fits within a specific template such as a groupof continuous densities corresponding to air next to a plurality ofdensities corresponding to a blood vessel. This may be useful when it isknown that, for example, a particular airway runs parallel to apulmonary artery, so, for a given length, the airway voxels should be inspecified proximity to pulmonary artery voxels.

One aspect of the present invention provides an iterative approach toregistration. In other words, registration is continually updated andrestarted, such that previous registration is being constantlydiscarded. This may be advantageous when, for example, navigating to avery distal portion of the lungs. Because the distal lungs moveconsiderably with the breathing cycle, registration that occurred closerto the main carina may not be relevant to the distant areas.Additionally, using this iterative approach, the potential inaccuracy isnot cumulative.

Another aspect of the present invention provides a continuous approach,as an alternative to the iterative approach, to registration. Thecontinuous approach involves the step-by-step correction of thepreviously performed transformation of voxel-based cavity features togeometry-based structures and shapes.

Another aspect of the present invention is that, by using a voxel-basedapproach, registration is actually accomplished by comparing anatomicalcavity features to cavity voxels, as opposed to anatomical shapes orlocations to structure shapes or locations. An advantage of thisapproach is that air-filled cavities are of a predictable, constantdensity. Conversely, tissue, especially lung tissue, is of a variable,less predictable density. One skilled in the art will see that all ofthe technology described herein applies equally well to the vasculatureof a patient. Blood-filled cavities, like air-filled cavities, are of apredictable, constant density.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method of the present invention; and

FIG. 2 is a flowchart of a more specific example of an embodiment of themethod of FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

Generally, the present invention includes a system and method forregistering a three-dimensional model of a body volume, such as a CTvolume, to a real-time image of a sensor. This registration methodcompares anatomical cavity features to cavity voxels, as opposed toanatomical shapes or locations to structure shapes or locations.

Referring now to the flowchart of FIG. 1, it is shown that the method ofthe present invention begins at 20 with a collection of reference data.This step involves the acquisition of a plurality of CT scans, which arethen assembled into a CT volume. During the procedure, the sensor isinserted into the lungs of the patient and a data stream is establishedbetween the sensor and a system processor.

At step 22, the data acquired is processed, which involves de-clutteringand digitization. Each of the voxels is assigned a number based on thetissue density Housefield number. This density value can be associatedwith gray level or color using well-known window-leveling techniques.The density is proportional to a probability that the sensor will occupya given voxel. The data is also filtered as desired. For example, if thesensor is advanced slowly rather than quickly, it will necessarilyresult in higher densities as any one voxel is going to be occupied fora longer period of time while the sensor takes longer to pass through.Hence, an advancement rate may be noted and used to normalize thedensities by speed, accordingly. After filtering, the voxels with higherdensities are given higher weight in registration than voxels havinglower densities.

At step 24 the desired parameters are defined. By way of example only,the voxel could be required to meet parameters such as: 1) falls withina particular density range, 2) falls within a predefined proximity froma currently accepted (registered) voxel, 3) fits within a specifictemplate such as a group of continuous densities corresponding to airnext to a plurality of densities corresponding to a blood vessel.

At 26, a compare and fit function is performed. This step includesmultiple sub-steps, beginning with step 30. These steps are performediteratively and repeatedly until the target is reached.

Step 30 involves an initial guess and is based on assumptions or knownlandmark techniques. For example, the main carina is relatively easy tomatch to the main carina of a BT.

At 32, the CT volume is registered to the sensor data using the initialguess and a difference between the two is calculated.

At 34, for each real voxel visited by the sensor, the registrationsoftware finds the closest voxel in the CT volume that matches specificparameters. The registration is then updated accordingly. If the processis iterative, the matched voxels may be aligned completely (ideally). Ifthe process is continuous, a density function is used to weight theimportance of that particular voxel match and the registration isadjusted, using frequency and/or density, a degree that is proportionalto the weighted importance.

Referring now to FIG. 2 for illustration purposes, there is shown a morespecific example of an embodiment of the method of FIG. 1, whichrepresents a binary voxel-based approach. At 60 a collection ofreference data is taken, similar to the data acquisition step 20described above. This step involves the acquisition of a plurality of CTscans, which are then assembled into a CT volume. The voxelsrepresenting internal lung air are then segmented from the CT volumeusing a known segmentation algorithm, obviating the need to extract thegeometry, surfaces, or structures of the lung. During the procedure, thesensor is inserted into the lungs of the patient and a data stream isestablished between the sensor and a system processor.

At step 62, the data acquired from the sensor is processed, whichinvolves de-cluttering and digitization. Each of the voxels is assigneda number based on the tissue density Housefield number. This densityvalue can be associated with gray level or color using well knownwindow-leveling techniques. The density is proportional to a probabilitythat the sensor will occupy a given voxel. The data is also filtered asdesired. For example, if the sensor is advanced slowly rather thanquickly, it will necessarily result in higher densities as any one voxelis going to be occupied for a longer period of time while the sensortakes longer to pass through. Hence, an advancement rate may be notedand used to adjust the densities accordingly. After filtering, thevoxels with higher densities are given higher registration importancethan voxels having lower densities.

At step 64 a threshold value is set for the sensing volume voxels. Forexample, if the density of a given voxel is higher than the thresholdvalue, that voxel is considered to be tissue and is given a value ofzero. If the density of the voxel is below the threshold, that voxel isconsidered to be air and is given a value of 1. Hence the voxel spacenow becomes a binary voxel space. This function is performed both on theCT volume as well as on the sensor data.

At step 66 a compare and fit function is performed. Because a binarysystem is being used, it is possible to use a variety of matchingmethods to register the two binary volumes. For example, a subtractionmethod could be used. A subtraction method superimposes a segment of thesensor data over a corresponding segment of the binary CT volume. Theregistration is effected by subtracting the binary values of the onevolume from the other. For example for any given voxel, if the valuesare both 1, when the aligned voxels are subtracted the value for thatmatched voxel space is zero. If they are not the same, however,subtraction results in either a 1 or a −1. All values are converted totheir absolute values and totaled. The registration of that particularsegment of sensor data is adjusted until a minimum subtracted total isacquired. One advantage of this method is that a minimum may be acquiredregardless of image quality.

Although the invention has been described in terms of particularembodiments and applications, one of ordinary skill in the art, in lightof this teaching, can generate additional embodiments and modificationswithout departing from the spirit of or exceeding the scope of theclaimed invention. Accordingly, it is to be understood that the drawingsand descriptions herein are proffered by way of example to facilitatecomprehension of the invention and should not be construed to limit thescope thereof.

1-20. (canceled)
 21. A method for registering a three-dimensional modelto an image of a sensor, the method comprising: inserting a sensor intoa body lumen to acquire location data of the sensor; assigning a densityvalue to each voxel of a plurality of voxels of a three-dimensionalmodel based on the acquired location data; determining a voxel of theplurality of voxels, which is closest to the sensor, based on aplurality of parameters, each of which has a predefined threshold; andregistering the closest voxel to the acquired location data.
 22. Themethod of claim 21, wherein the density value is based on an advancementspeed of the sensor.
 23. The method of claim 21, wherein an advancementspeed is inversely proportional to the density value is.
 24. The methodof claim 21, wherein the density value is a Hounsfield number.
 25. Themethod of claim 21, wherein the density value to each voxel isproportional to a probability that the sensor occupies each voxel. 26.The method of claim 21, further comprising determining whether a voxelis tissue or air.
 27. The method of claim 26, wherein a voxel isdetermined as tissue when the density value is higher than a threshold.28. The method of claim 26, wherein a voxel is determined as airway whenthe density value is lower than a threshold.
 29. The method of claim 21,wherein registering the voxel includes determining whether the densityvalue of the voxel falls within a predetermined range.
 30. The method ofclaim 21, wherein registering the voxel includes determining whether thevoxel falls within a predefined proximity from a recently registeredvoxel.
 31. The method of claim 21, wherein registering the closest voxelincludes determining whether the density value fits with a template. 32.The method of claim 31, wherein the template is a group of continuousdensities corresponding to air next to a plurality of densitiescorresponding to a blood vessel.
 33. The method of claim 21, whereinregistering the closest voxel includes: making a guess based on a knownlandmark; temporally registering a voxel to the acquired location databased on the guess; and determining the closest voxel that matches theplurality of parameters.
 34. The method of claim 33, further comprisingcalculating a difference between the temporally registered voxel and theacquired location data.
 35. The method of claim 34, wherein the closestvoxel is determined based on the guess and the difference.
 36. A methodfor registering a three-dimensional model to a sensing volume of asensor, the method comprising: segmenting a plurality of voxels of thethree-dimensional model; inserting a sensor into a body lumen to acquiresensing volume voxels from the sensor; assigning a density value foreach voxel of the sensing volume voxels; and segmenting the sensingvolume voxels based on a threshold; and registering a first portion ofthe plurality of voxels of the three-dimensional model to the sensingvolume voxels.
 37. The method of claim 36, wherein the density value isbased on an advancement speed of the sensor.
 38. The method of claim 36,wherein an advancement speed is inversely proportional to the densityvalue.
 39. The method of claim 36, wherein the density value is aHounsfield number.
 40. The method of claim 36, wherein the density valueof a voxel is proportional to a probability that the sensor occupies thevoxel.
 41. The method of claim 36, wherein a voxel is considered astissue when a density value of the voxel is higher than the threshold.42. The method of claim 36, wherein a voxel is considered as airway whena density value of the voxel is lower than the threshold.
 43. The methodof claim 36, wherein registering the first portion includes: calculatinga difference between a value of each segmented voxel of the firstportion and a value of a corresponding segmented voxel of the sensingvolume voxels; and summing differences between the first portion and thesensing volume voxels to obtain a total value.
 44. The method of claim43, wherein registering the first portion further includes: determiningthe first portion, which gives a minimum total value; and registeringthe first portion, which gives the minimum total value, to the sensingvolume voxels.